AI Engineer 5 – AI Foundations, LLM Core, Agentic AI

Capital One

. Partner with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products .

Posted 9/18/2026full-timeCambridge • California • United StatesMid-LevelSenior💰 $229,900 - $286,200 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and optimizing AI systems, including foundation model training and LLM inference, while ensuring compliance with ethical AI standards. Proven ability to lead technical teams and manage cost-performance governance for scalable AI solutions.

Highest-signal resume keywords

AI Systems DevelopmentPython ProgrammingAWS Cloud DeploymentLLM InferenceModel Optimization

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI AlgorithmsMachine Learning TechnologiesFoundation Model TrainingMulti-Agent WorkflowsSimilarity SearchModel EvaluationExperimentationGovernanceObservabilityDynamic Inference Strategies

Soft Skills

Excellent CommunicationPresentation SkillsMentoring

Tools & Technologies

AWS UltraclustersHugging FaceVectorDBsPyTorchGoogle CloudAzure

Industry Keywords

Ethical AI DeploymentExplainabilityFairnessHuman-in-the-Loop ReviewCost Efficiency

Tech Stack

Tools & technologies

AWSAzureCloudJavaPythonPyTorchScalaC++Go

About the role

Key responsibilities & impact

Partner with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products
Design, develop, test, deploy, and support AI software components, including foundation model training, LLM inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability
Leverage open-source and SaaS AI technologies such as AWS Ultraclusters, Hugging Face, VectorDBs, and PyTorch
Invent and introduce foundation-model optimization techniques to improve scalability, cost, latency, and throughput of production AI systems
Contribute to the technical vision and long-term roadmap for foundational AI systems
Design, implement, and optimize multi-model orchestration pipelines integrating LLMs, vector search, and domain-specific models
Establish and lead cost-performance governance reviews, tracking GPU utilization, model throughput, and inference cost efficiency
Lead design councils or review boards to ensure technical consistency and compliance with AI engineering standards
Mentor Principal- and Manager-level AI engineers and elevate organizational technical maturity

Requirements

What you’ll need

Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 6 years of experience developing AI and ML algorithms or technologies; OR a Master's degree in those fields plus at least 4 years of such experience
At least 6 years of experience programming with Python, Go, Scala, CUDA, or Java
Experience leading AI systems development with cost, latency, throughput, and accuracy tradeoffs
7 years of experience deploying scalable and responsible AI solutions on cloud platforms such as AWS, Google Cloud, Azure, or equivalent private cloud
Experience designing, developing, delivering, and supporting complex AI systems
Experience developing AI and ML algorithms or technologies including LLM inference, similarity search, VectorDBs, guardrails, and memory using Python, C++, C#, Java, CUDA, or Golang
Experience optimizing training and inference software for hardware utilization, latency, throughput, and cost
Experience building agentic AI systems and workflows
Experience architecting and integrating rule-based, retrieval-augmented, and generative components into unified production pipelines
Experience defining and enforcing standards for ethical AI deployment, including explainability, fairness, and human-in-the-loop review
Demonstrated ability to balance model performance and operational cost through dynamic inference strategies and model compression
Experience right-sizing models, instance counts, and hardware types for requirements such as context length and token inputs/outputs
Excellent communication and presentation skills
Capital One will consider sponsoring a new qualified applicant for employment authorization

Benefits

Comp & perks

Performance-based incentive compensation, which may include cash bonuses and/or long-term incentives (LTI)
Comprehensive, competitive, and inclusive health, financial, and other benefits supporting total well-being
Reasonable accommodations for applicants with disabilities
Employment authorization sponsorship may be considered for a new qualified applicant